Large data sets may exist in various sizes and levels of organization. With big data comprising data sets as large as ever, the volume of data collected incident to the increased popularity of online and electronic transactions continues to grow. Billions of rows and hundreds of thousands of columns worth of data may populate a single table, for example. An example of the use of big data is in identifying and categorizing business spending and consumer spending, which is frequently a key priority for transaction card issuers. However, transactions processed by the transaction card issuer are massive in volume and comprise tremendously large data sets. Companies frequently desire to process and analyze this data; however, such processing and analysis is typically time consuming and resource intensive due to the volume of data. These limitations confuse and frustrate the identification and categorization of transaction data, while also hampering data analytics.